Emotional fine-tuning of assignments

Hans Tilstra, Senior Learning Designer
 
As learning design trends towards more immersive and authentic assignments, it is accompanied by the hope that students will embrace these challenges, deal positively with ambiguities, pursue deep learning strategies and rate such assignments highly in surveys.
However, when students respond to assignments with less enthusiasm, it can be tempting to swing back to the more didactic approaches, making assignments easier and make answers more readily available. At its worst, disappointment with feedback can lead to collusive mediocrity, where the assignment is designed to create a short-term ‘win-win’ for both student and teacher.
As we consider learning designs on a scale between the didactic and the more ‘constructivist’, one question relates to the optimal pitch of an assignment. How challenging should an assignment be, and what are key clues that an optimal pitch is reached?
To draw on the analogy of dosage of medicine, an overdose is considered toxic, the dosage becomes therapeutic when we get the amount right, and non-therapeutic when we take too little.
Similarly, when learning to read, students need to be given books at a helpful level of difficulty. So, when the reader grasps less than 95% of reading material, this is considered a ‘frustration level’.  When the reader grasps more than 95% of a page, the level is considered to be at an ‘instructional’ level, needing guidance. If the reader grasps 98% or more, they are deemed to be at an ‘independence’ level, as the remaining 2% can be figured out by context.
Mihaly Csikszentmihalyi has described the optimal learning condition as points between boredom and anxiety (graph by Ryan Lewis).

This framework can be used to ‘take the pulse’ in an assignment (an Experience Sampling Method), and subsequent questions can help pinpoint where the level of a challenge matches the skill levels students bring to the assignment. The aim of this fine-tuning is for students to work towards a psychological state of ‘flow’, described as an optimal learning condition.
 
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